Bone age assessment based on three-dimensional ultrasound and artificial intelligence compared with paediatrician-read radiographic bone age: protocol for a prospective, diagnostic accuracy study

• Published in: BMJ open Vol. 14; no. 2; p. e079969
• Main Authors: Chen, Li, Zeng, Bolun, Shen, Jian, Xu, Jiangchang, Cai, Zehang, Su, Shudian, Chen, Jie, Cai, Xiaojun, Ying, Tao, hu, bing, Wu, Min, Chen, Xiaojun, Zheng, Yuanyi
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 24.02.2024; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Protocol
• Subjects: Accuracy; Adult; Age; Artificial Intelligence; Automation; Bones; Child; Child protection; China; Diagnostic Imaging; Hospitals; Humans; Medical diagnosis; Neural networks; Neural Networks, Computer; Paediatric radiology; PAEDIATRICS; Pediatrics; Prospective Studies; Puberty; Quality standards; Radiation; Radiography; Radiology and Imaging; Reproducibility of Results; Ultrasonic imaging; X-rays; China; United States > US; Israel; Child protection; Diagnostic Imaging; PAEDIATRICS; Paediatric radiology
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2023-079969

IntroductionRadiographic bone age (BA) assessment is widely used to evaluate children’s growth disorders and predict their future height. Moreover, children are more sensitive and vulnerable to X-ray radiation exposure than adults. The purpose of this study is to develop a new, safer, radiation-free BA assessment method for children by using three-dimensional ultrasound (3D-US) and artificial intelligence (AI), and to test the diagnostic accuracy and reliability of this method.Methods and analysisThis is a prospective, observational study. All participants will be recruited through Paediatric Growth and Development Clinic. All participants will receive left hand 3D-US and X-ray examination at the Shanghai Sixth People’s Hospital on the same day, all images will be recorded. These image related data will be collected and randomly divided into training set (80% of all) and test set (20% of all). The training set will be used to establish a cascade network of 3D-US skeletal image segmentation and BA prediction model to achieve end-to-end prediction of image to BA. The test set will be used to evaluate the accuracy of AI BA model of 3D-US. We have developed a new ultrasonic scanning device, which can be proposed to automatic 3D-US scanning of hands. AI algorithms, such as convolutional neural network, will be used to identify and segment the skeletal structures in the hand 3D-US images. We will achieve automatic segmentation of hand skeletal 3D-US images, establish BA prediction model of 3D-US, and test the accuracy of the prediction model.Ethics and disseminationThe Ethics Committee of Shanghai Sixth People’s Hospital approved this study. The approval number is 2022-019. A written informed consent will be obtained from their parent or guardian of each participant. Final results will be published in peer-reviewed journals and presented at national and international conferences.Trial registration numberChiCTR2200057236.